a16z的报告里举了几个例子，把这个问题讲得很具体。投行分析师用Hebbia，几百份公开文件自动分析完，财务模型直接生成，以前要熬几个通宵做的事情，现在可以去睡觉了。医生用Abridge，它能实时记录医患对话，自动整理病历和后续跟进事项，医生看诊时不用再一边问话一边盯着屏幕敲字。还有做财务对账的Basis，跨系统自动核对试算表，原本需要人工反复比对的工作变成几分钟的事。
Secret Sauce #2: Adaptive Routing
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Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
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